{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "import numpy as np\n",
      "from scipy.stats import gaussian_kde\n",
      "import brewer2mpl\n",
      "\n",
      "set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\n",
      "\n",
      "def violinplot(ax, x, ys, bp=False, cut=False, facecolor=set2[0],\n",
      "               edgecolor=ppl.almost_black,\n",
      "               alpha=0.3, bw_method=0.05, width=None):\n",
      "    \"\"\"Make a violin plot of each dataset in the `ys` sequence. `ys` is a\n",
      "    list of numpy arrays.\n",
      "    Adapted by: Olga Botvinnik\n",
      "    # Original Author: Teemu Ikonen <tpikonen@gmail.com>\n",
      "    # Based on code by Flavio Codeco Coelho,\n",
      "    # http://pyinsci.blogspot.com/2009/09/violin-plot-with-matplotlib.html\n",
      "    \"\"\"\n",
      "    dist = np.max(x) - np.min(x)\n",
      "    if width is None:\n",
      "        width = min(0.15 * max(dist, 1.0), 0.4)\n",
      "    for i, (d, p) in enumerate(zip(ys, x)):\n",
      "        k = gaussian_kde(d, bw_method=bw_method) #calculates the kernel density\n",
      "        #         k.covariance_factor = 0.1\n",
      "        s = 0.0\n",
      "        if not cut:\n",
      "            s = 1 * np.std(d) #FIXME: magic constant 1\n",
      "        m = k.dataset.min() - s #lower bound of violin\n",
      "        M = k.dataset.max() + s #upper bound of violin\n",
      "        x = np.linspace(m, M, 100) # support for violin\n",
      "        v = k.evaluate(x) #violin profile (density curve)\n",
      "        v = width * v / v.max() #scaling the violin to the available space\n",
      "        if isinstance(facecolor, list):\n",
      "        #             for x0, v0, p0\n",
      "            ax.fill_betweenx(x, -v + p,\n",
      "                             v + p,\n",
      "                             facecolor=facecolor[i],\n",
      "                             alpha=alpha, edgecolor=edgecolor)\n",
      "        else:\n",
      "            ax.fill_betweenx(x, -v + p,\n",
      "                             v + p,\n",
      "                             facecolor=facecolor,\n",
      "                             alpha=alpha, edgecolor=edgecolor)\n",
      "    if bp:\n",
      "        ax.boxplot(ys, notch=1, positions=x, vert=1)\n",
      "    ppl.remove_chartjunk(ax, ['top', 'right'])\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[50:100::10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-4-9d824d02bf48>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-4-9d824d02bf48>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    [50:100::10]\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = 5\n",
      "x = range(n)\n",
      "ys = [np.random.randn(np.random.choice(range(50, 110, 10)))+_ for _ in range(n)]\n",
      "\n",
      "fig, ax = plt.subplots(1)\n",
      "violinplot(ax, x, ys)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x2aaaacb49fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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S5Ace7ZEsyzKJVApSKTzPY2p+EZ8sMzL09LalB43P59taFOzv68PzPObvzfPL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\nEZFCscCJiBSKBU5EpFAscCIihfofKJNlkYG1CM8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1559c990>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Change the bandwidth size (bigger = smoother) with `bw_method` (default `bw_method=0.1`, but you can specify any `bw_method` to http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html#scipy.stats.gaussian_kde:\n",
      "\n",
      "\n",
      "Parameters :\t\n",
      "dataset : array_like\n",
      "Datapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2-D array with shape (# of dims, # of data).\n",
      "bw_method : str, scalar or callable, optional\n",
      "The method used to calculate the estimator bandwidth. This can be \u2018scott\u2019, \u2018silverman\u2019, a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a gaussian_kde instance as only parameter and return a scalar. If None (default), \u2018scott\u2019 is used. See Notes for more details."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1)\n",
      "violinplot(ax, x, ys, bw_method=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x2aaaacb58c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2aaaace82510>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}